South_Africa_ts <- tsclean(South_Africa_ts)
South_Africa_ts
# Identification: Plotting the Time Series Data
plot(South_Africa_ts)
# Estimating the appropriate model
South_Africa_ts_model <- auto.arima(South_Africa_ts)
South_Africa_ts_model
# Forecasting
options(max.print=1000000)
South_Africa_ts_forecast <- forecast (South_Africa_ts_model, level=c(95), h=255)
plot(South_Africa_ts_forecast)
South_Africa_ts_forecast
# Exporting
write.table(South_Africa_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/SOUTH_AFRICA_TSA.csv", sep=",")
# Importing Data
AUSTRIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "D1:D239")
# Checking the Imported Data
View(AUSTRIA)
# Creating Time Series Data
Austria_ts <- ts(AUSTRIA, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
Austria_ts
sum(is.na(Austria_ts))
library(forecast)
Austria_ts <- tsclean(Austria_ts)
Austria_ts
# Identification: Plotting the Time Series Data
plot(Austria_ts)
# Estimating the appropriate model
Austria_ts_model <- auto.arima(Austria_ts)
Austria_ts_model
# Forecasting
options(max.print=1000000)
Austria_ts_forecast <- forecast (Austria_ts_model, level=c(95), h=255)
plot(Austria_ts_forecast)
Austria_ts_forecast
# Exporting
write.table(Austria_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/AUSTRIA_TSA.csv", sep=",")
# Importing Data
SAUDI<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "AC1:AC239")
# Checking the Imported Data
View(SAUDI)
# Creating Time Series Data
Saudi_ts <- ts(SAUDI, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
Saudi_ts
sum(is.na(Saudi_ts))
library(forecast)
Saudi_ts <- tsclean(Saudi_ts)
Saudi_ts
# Identification: Plotting the Time Series Data
plot(Saudi_ts)
# Estimating the appropriate model
Saudi_ts_model <- auto.arima(Saudi_ts)
Saudi_ts_model
# Forecasting
options(max.print=1000000)
Saudi_ts_forecast <- forecast (Saudi_ts_model, level=c(95), h=255)
plot(Saudi_ts_forecast)
Saudi_ts_forecast
# Exporting
write.table(Saudi_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/SAUDI_TSA.csv", sep=",")
# Importing Data
CZECK<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "I1:I239")
# Checking the Imported Data
View(CZECK)
# Creating Time Series Data
Czeck_ts <- ts(CZECK, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
Czeck_ts
sum(is.na(Czeck_ts))
library(forecast)
Czeck_ts <- tsclean(Czeck_ts)
Czeck_ts
# Identification: Plotting the Time Series Data
plot(Czeck_ts)
# Estimating the appropriate model
Czeck_ts_model <- auto.arima(Czeck_ts)
Czeck_ts_model
# Forecasting
options(max.print=1000000)
Czeck_ts_forecast <- forecast (Czeck_ts_model, level=c(95), h=255)
plot(Czeck_ts_forecast)
Czeck_ts_forecast
# Exporting
write.table(Czeck_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/CZECK_TSA.csv", sep=",")
# Importing Data
PAKISTAN<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "Y1:Y239")
# Checking the Imported Data
View(PAKISTAN)
# Creating Time Series Data
Pakistan_ts <- ts(PAKISTAN, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
Pakistan_ts
sum(is.na(Pakistan_ts))
library(forecast)
Pakistan_ts <- tsclean(Pakistan_ts)
Pakistan_ts
# Identification: Plotting the Time Series Data
plot(Pakistan_ts)
# Estimating the appropriate model
Pakistan_ts_model <- auto.arima(Pakistan_ts)
Pakistan_ts_model
# Forecasting
options(max.print=1000000)
Pakistan_ts_forecast <- forecast (Pakistan_ts_model, level=c(95), h=255)
plot(Pakistan_ts_forecast)
Pakistan_ts_forecast
# Creating Time Series Data
Pakistan_ts <- ts(PAKISTAN, start=c(2000,1), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
Pakistan_ts
sum(is.na(Pakistan_ts))
library(forecast)
Pakistan_ts <- tsclean(Pakistan_ts)
Pakistan_ts
# Identification: Plotting the Time Series Data
plot(Pakistan_ts)
# Estimating the appropriate model
Pakistan_ts_model <- auto.arima(Pakistan_ts)
Pakistan_ts_model
# Forecasting
options(max.print=1000000)
Pakistan_ts_forecast <- forecast (Pakistan_ts_model, level=c(95), h=258)
plot(Pakistan_ts_forecast)
Pakistan_ts_forecast
# Creating Time Series Data
Pakistan_ts <- ts(PAKISTAN, start=c(2000,1), end=c(2018,11), frequency=12)
# Viewing and Checking the Created Time Series Data
Pakistan_ts
sum(is.na(Pakistan_ts))
library(forecast)
Pakistan_ts <- tsclean(Pakistan_ts)
Pakistan_ts
# Identification: Plotting the Time Series Data
plot(Pakistan_ts)
# Estimating the appropriate model
Pakistan_ts_model <- auto.arima(Pakistan_ts)
Pakistan_ts_model
# Forecasting
options(max.print=1000000)
Pakistan_ts_forecast <- forecast (Pakistan_ts_model, level=c(95), h=265)
plot(Pakistan_ts_forecast)
Pakistan_ts_forecast
# Exporting
write.table(Pakistan_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/PAKISTAN_TSA.csv", sep=",")
# Importing Data
SWEDEN<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "AG1:AG239")
# Checking the Imported Data
View(SWEDEN)
# Creating Time Series Data
Sweden_ts <- ts(SWEDEN, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
Sweden_ts
sum(is.na(Sweden_ts))
library(forecast)
Sweden_ts <- tsclean(Sweden_ts)
Sweden_ts
# Identification: Plotting the Time Series Data
plot(Sweden_ts)
# Estimating the appropriate model
Sweden_ts_model <- auto.arima(Sweden_ts)
Sweden_ts_model
# Forecasting
options(max.print=1000000)
Sweden_ts_forecast <- forecast (Sweden_ts_model, level=c(95), h=255)
plot(Sweden_ts_forecast)
Sweden_ts_forecast
# Exporting
write.table(Sweden_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/SWEDEN_TSA.csv", sep=",")
# Importing Data
UAE<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "AL1:AL239")
# Checking the Imported Data
View(UAE)
# Creating Time Series Data
UAE_ts <- ts(UAE, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
UAE_ts
sum(is.na(UAE_ts))
library(forecast)
UAE_ts <- tsclean(UAE_ts)
UAE_ts
# Identification: Plotting the Time Series Data
plot(UAE_ts)
# Estimating the appropriate model
UAE_ts_model <- auto.arima(UAE_ts)
UAE_ts_model
# Forecasting
options(max.print=1000000)
UAE_ts_forecast <- forecast (UAE_ts_model, level=c(95), h=255)
plot(UAE_ts_forecast)
UAE_ts_forecast
# Exporting
write.table(UAE_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/UAE_TSA.csv", sep=",")
# Importing Data
THAILAND<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "AI1:AI239")
# Checking the Imported Data
View(THAILAND)
# Creating Time Series Data
THAILAND_ts <- ts(THAILAND, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
THAILAND_ts
sum(is.na(THAILAND_ts))
library(forecast)
THAILAND_ts <- tsclean(THAILAND_ts)
THAILAND_ts
# Identification: Plotting the Time Series Data
plot(THAILAND_ts)
# Estimating the appropriate model
THAILAND_ts_model <- auto.arima(THAILAND_ts)
THAILAND_ts_model
# Forecasting
options(max.print=1000000)
THAILAND_ts_forecast <- forecast (THAILAND_ts_model, level=c(95), h=255)
plot(THAILAND_ts_forecast)
THAILAND_ts_forecast
# Exporting
write.table(THAILAND_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/THAILAND_TSA.csv", sep=",")
# Importing Data
TAIWAN<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "AH1:AH239")
# Checking the Imported Data
View(TAIWAN)
# Creating Time Series Data
TAIWAN_ts <- ts(TAIWAN, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
TAIWAN_ts
sum(is.na(TAIWAN_ts))
library(forecast)
TAIWAN_ts <- tsclean(TAIWAN_ts)
TAIWAN_ts
# Identification: Plotting the Time Series Data
plot(TAIWAN_ts)
# Estimating the appropriate model
TAIWAN_ts_model <- auto.arima(TAIWAN_ts)
TAIWAN_ts_model
# Forecasting
options(max.print=1000000)
TAIWAN_ts_forecast <- forecast (TAIWAN_ts_model, level=c(95), h=255)
plot(TAIWAN_ts_forecast)
TAIWAN_ts_forecast
# Exporting
write.table(TAIWAN_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/TAIWAN_TSA.csv", sep=",")
# Importing Data
QATAR<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "AA1:AA239")
# Checking the Imported Data
View(QATAR)
# Creating Time Series Data
QATAR_ts <- ts(QATAR, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
QATAR_ts
sum(is.na(QATAR_ts))
library(forecast)
QATAR_ts <- tsclean(QATAR_ts)
QATAR_ts
# Identification: Plotting the Time Series Data
plot(QATAR_ts)
# Estimating the appropriate model
QATAR_ts_model <- auto.arima(QATAR_ts)
QATAR_ts_model
# Forecasting
options(max.print=1000000)
QATAR_ts_forecast <- forecast (QATAR_ts_model, level=c(95), h=255)
plot(QATAR_ts_forecast)
QATAR_ts_forecast
# Exporting
write.table(QATAR_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/QATAR_TSA.csv", sep=",")
# Importing Data
HUNGARY<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "M1:M239")
# Checking the Imported Data
View(HUNGARY)
# Creating Time Series Data
HUNGARY_ts <- ts(HUNGARY, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
HUNGARY_ts
sum(is.na(HUNGARY_ts))
library(forecast)
HUNGARY_ts <- tsclean(HUNGARY_ts)
HUNGARY_ts
# Identification: Plotting the Time Series Data
plot(HUNGARY_ts)
# Estimating the appropriate model
HUNGARY_ts_model <- auto.arima(HUNGARY_ts)
HUNGARY_ts_model
# Forecasting
options(max.print=1000000)
HUNGARY_ts_forecast <- forecast (HUNGARY_ts_model, level=c(95), h=255)
plot(HUNGARY_ts_forecast)
HUNGARY_ts_forecast
# Creating Time Series Data
HUNGARY_ts <- ts(HUNGARY, start=c(2000,1), end=c(2018,12), frequency=12)
# Viewing and Checking the Created Time Series Data
HUNGARY_ts
sum(is.na(HUNGARY_ts))
library(forecast)
HUNGARY_ts <- tsclean(HUNGARY_ts)
HUNGARY_ts
# Identification: Plotting the Time Series Data
plot(HUNGARY_ts)
# Estimating the appropriate model
HUNGARY_ts_model <- auto.arima(HUNGARY_ts)
HUNGARY_ts_model
# Forecasting
options(max.print=1000000)
HUNGARY_ts_forecast <- forecast (HUNGARY_ts_model, level=c(95), h=264)
plot(HUNGARY_ts_forecast)
HUNGARY_ts_forecast
# Creating Time Series Data
HUNGARY_ts <- ts(HUNGARY, start=c(2000,1), end=c(2019,03), frequency=12)
# Viewing and Checking the Created Time Series Data
HUNGARY_ts
sum(is.na(HUNGARY_ts))
library(forecast)
HUNGARY_ts <- tsclean(HUNGARY_ts)
HUNGARY_ts
# Identification: Plotting the Time Series Data
plot(HUNGARY_ts)
# Estimating the appropriate model
HUNGARY_ts_model <- auto.arima(HUNGARY_ts)
HUNGARY_ts_model
# Forecasting
options(max.print=1000000)
HUNGARY_ts_forecast <- forecast (HUNGARY_ts_model, level=c(95), h=261)
plot(HUNGARY_ts_forecast)
HUNGARY_ts_forecast
# Creating Time Series Data
HUNGARY_ts <- ts(HUNGARY, start=c(2000,1), end=c(2019,09), frequency=12)
# Viewing and Checking the Created Time Series Data
HUNGARY_ts
sum(is.na(HUNGARY_ts))
library(forecast)
HUNGARY_ts <- tsclean(HUNGARY_ts)
HUNGARY_ts
# Identification: Plotting the Time Series Data
plot(HUNGARY_ts)
# Estimating the appropriate model
HUNGARY_ts_model <- auto.arima(HUNGARY_ts)
HUNGARY_ts_model
# Forecasting
options(max.print=1000000)
HUNGARY_ts_forecast <- forecast (HUNGARY_ts_model, level=c(95), h=255)
plot(HUNGARY_ts_forecast)
HUNGARY_ts_forecast
# Creating Time Series Data
HUNGARY_ts <- ts(HUNGARY, start=c(2000,1), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
HUNGARY_ts
sum(is.na(HUNGARY_ts))
library(forecast)
HUNGARY_ts <- tsclean(HUNGARY_ts)
HUNGARY_ts
# Identification: Plotting the Time Series Data
plot(HUNGARY_ts)
# Estimating the appropriate model
HUNGARY_ts_model <- auto.arima(HUNGARY_ts)
HUNGARY_ts_model
# Forecasting
options(max.print=1000000)
HUNGARY_ts_forecast <- forecast (HUNGARY_ts_model, level=c(95), h=258)
plot(HUNGARY_ts_forecast)
HUNGARY_ts_forecast
# Exporting
write.table(HUNGARY_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/HUNGARY_TSA.csv", sep=",")
# Importing Data
INDONESIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "O1:O239")
# Checking the Imported Data
View(INDONESIA)
# Creating Time Series Data
INDONESIA_ts <- ts(INDONESIA, start=c(2000,1), end=c(2018 ,12), frequency=12)
# Viewing and Checking the Created Time Series Data
INDONESIA_ts
sum(is.na(INDONESIA_ts))
library(forecast)
INDONESIA_ts <- tsclean(INDONESIA_ts)
INDONESIA_ts
# Identification: Plotting the Time Series Data
plot(INDONESIA_ts)
# Estimating the appropriate model
INDONESIA_ts_model <- auto.arima(INDONESIA_ts)
INDONESIA_ts_model
# Forecasting
options(max.print=1000000)
INDONESIA_ts_forecast <- forecast (INDONESIA_ts_model, level=c(95), h=264)
plot(INDONESIA_ts_forecast)
INDONESIA_ts_forecast
# Exporting
write.table(INDONESIA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/INDONESIA_TSA.csv", sep=",")
# Importing Data
SLOVAKIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "AD1:AD239")
# Checking the Imported Data
View(SLOVAKIA)
# Creating Time Series Data
SLOVAKIA_ts <- ts(SLOVAKIA, start=c(2000,1), end=c(2018 ,12), frequency=12)
# Viewing and Checking the Created Time Series Data
SLOVAKIA_ts
sum(is.na(SLOVAKIA_ts))
library(forecast)
SLOVAKIA_ts <- tsclean(SLOVAKIA_ts)
SLOVAKIA_ts
# Identification: Plotting the Time Series Data
plot(SLOVAKIA_ts)
# Estimating the appropriate model
SLOVAKIA_ts_model <- auto.arima(SLOVAKIA_ts)
SLOVAKIA_ts_model
# Forecasting
options(max.print=1000000)
SLOVAKIA_ts_forecast <- forecast (SLOVAKIA_ts_model, level=c(95), h=264)
plot(SLOVAKIA_ts_forecast)
SLOVAKIA_ts_forecast
# Exporting
write.table(SLOVAKIA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/SLOVAKIA_TSA.csv", sep=",")
# Importing Data
ISRAEL<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "Q1:Q239")
# Checking the Imported Data
View(ISRAEL)
# Creating Time Series Data
ISRAEL_ts <- ts(ISRAEL, start=c(2000,1), end=c(2018 ,12), frequency=12)
# Viewing and Checking the Created Time Series Data
ISRAEL_ts
sum(is.na(ISRAEL_ts))
library(forecast)
ISRAEL_ts <- tsclean(ISRAEL_ts)
ISRAEL_ts
# Identification: Plotting the Time Series Data
plot(ISRAEL_ts)
# Estimating the appropriate model
ISRAEL_ts_model <- auto.arima(ISRAEL_ts)
ISRAEL_ts_model
# Forecasting
options(max.print=1000000)
ISRAEL_ts_forecast <- forecast (ISRAEL_ts_model, level=c(95), h=264)
plot(ISRAEL_ts_forecast)
ISRAEL_ts_forecast
# Exporting
write.table(ISRAEL_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/ISRAEL_TSA.csv", sep=",")
# Importing Data
JORDAN<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "T1:T239")
# Checking the Imported Data
View(JORDAN)
# Creating Time Series Data
JORDAN_ts <- ts(JORDAN, start=c(2000,1), end=c(2018 ,12), frequency=12)
# Viewing and Checking the Created Time Series Data
JORDAN_ts
sum(is.na(JORDAN_ts))
library(forecast)
JORDAN_ts <- tsclean(JORDAN_ts)
JORDAN_ts
# Identification: Plotting the Time Series Data
plot(JORDAN_ts)
# Estimating the appropriate model
JORDAN_ts_model <- auto.arima(JORDAN_ts)
JORDAN_ts_model
# Forecasting
options(max.print=1000000)
JORDAN_ts_forecast <- forecast (JORDAN_ts_model, level=c(95), h=264)
plot(JORDAN_ts_forecast)
JORDAN_ts_forecast
# Exporting
write.table(JORDAN_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/JORDAN_TSA.csv", sep=",")
# Importing Data
MOROCCO<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "W1:W239")
# Checking the Imported Data
View(MOROCCO)
# Creating Time Series Data
MOROCCO_ts <- ts(MOROCCO, start=c(2000,1), end=c(2018 ,12), frequency=12)
# Viewing and Checking the Created Time Series Data
MOROCCO_ts
sum(is.na(MOROCCO_ts))
library(forecast)
MOROCCO_ts <- tsclean(MOROCCO_ts)
MOROCCO_ts
# Identification: Plotting the Time Series Data
plot(MOROCCO_ts)
# Estimating the appropriate model
MOROCCO_ts_model <- auto.arima(MOROCCO_ts)
MOROCCO_ts_model
# Forecasting
options(max.print=1000000)
MOROCCO_ts_forecast <- forecast (MOROCCO_ts_model, level=c(95), h=264)
plot(MOROCCO_ts_forecast)
MOROCCO_ts_forecast
# Importing Data
MOROCCO<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "W1:W239")
# Checking the Imported Data
View(MOROCCO)
# Importing Data
MOROCCO<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "W1:W239")
# Checking the Imported Data
View(MOROCCO)
# Importing Data
MOROCCO<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Steel/TSA.xlsx",sheet = "Sheet1", range = "W1:W239")
# Checking the Imported Data
View(MOROCCO)
# Creating Time Series Data
MOROCCO_ts <- ts(MOROCCO, start=c(2005,1), end=c(2018 ,12), frequency=12)
# Viewing and Checking the Created Time Series Data
MOROCCO_ts
sum(is.na(MOROCCO_ts))
library(forecast)
MOROCCO_ts <- tsclean(MOROCCO_ts)
MOROCCO_ts
# Identification: Plotting the Time Series Data
plot(MOROCCO_ts)
# Estimating the appropriate model
MOROCCO_ts_model <- auto.arima(MOROCCO_ts)
MOROCCO_ts_model
# Forecasting
options(max.print=1000000)
MOROCCO_ts_forecast <- forecast (MOROCCO_ts_model, level=c(95), h=264)
plot(MOROCCO_ts_forecast)
MOROCCO_ts_forecast
# Exporting
write.table(MOROCCO_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Steel/MOROCCO_TSA.csv", sep=",")
